# Factorial ANCOVA MANCOVA Models

Factorial designs and analysis of covariance models; builds a linear model to include main-effects and interactions for categorical predictors (to a specified degree, e.g., two-way effects, three-way effects, etc.). Both univariate (single continuous dependent variable) and multivariate (multiple continuous dependent variables) designs can be analyzed. Default results include the ANOVA/ANCOVA (MANOVA/MANCOVA) table; set the Level of detail parameter to All results to request tables of means and other statistics.

Model and Estimation

Parameterization of effects. Specifies either the sigma-restricted model or the overparameterized model; the sigma restricted parameterization is the default.

Factorial to degree. Specifies the factorial degree of the design to be tested; Statistica will construct a factorial design for all categorical predictors up to the specified degree (i.e., by default up to degree 2, so that the final model will include all factor main effects and two-way interactions for categorical predictors).

Type of sums of squares. Specifies how to construct the hypotheses for the tests of main effects and interactions. Note: Type IV sums of squares are not available for sigma-restricted parameterization; Type VI sums of squares are not available for overparameterized parameterization of categorical factor effects.

Intercept. Specifies whether the intercept (constant) is to be included in the model.

Lack of fit. Requests the computation of pure error for testing the lack-of-fit hypothesis.

Sweep delta 1.E-. Specifies the negative exponent for a base-10 constant Delta (delta = 10^-sdelta); the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.

Inverse delta 1.E-. Specifies the negative exponent for a base-10 constant Delta (delta = 10^-idelta); the default value is 12. Delta is used to check for matrix singularity in matrix inversion calculations.

Results

Detail of computed results reported. Specifies the detail of computed results reported. If All results is requested, Statistica will also report all univariate results (for multivariate designs), descriptive statistics, details about the design terms, the whole-model R, regression coefficients, and the least-squares means for all effects. Residual and predicted statistics (for observations) can be requested as options.

Least square means. Creates the expected marginal means, given the current model; either all marginal means tables can be computed, or only the means for the highest-order effect of the factorial design.

Post Hoc Tests. Performs post-hoc comparisons between the means in the design.

Tests homogeneity of variances. Tests the homogeneity of variances/covariances assumption. One of the assumptions of univariate ANOVA is that the variances are equal (homogeneous) across the cells of the between-groups design. In the multivariate case (MANOVA), this assumption applies to the variance/covariance matrix of dependent variables (and covariates).

Plots of means vs. std. dev. Plots the (unweighted) marginal means (see also the Means tab) for the selected Variables against the standard deviations.

Contrast coefficients. Specifies your contrasts for least squares means; consult the Electronic Manual for syntax details.

Residual Analysis

Residual analysis. In addition to the predicted, observed, and residual values, Statistica will compute the (default) 95% Prediction intervals and 95% Confidence limits, the Standardized predicted and Standardized residual score, the Leverage values, the Deleted residual and Studentized deleted residual scores, Mahalanobis and Cook distance scores, the DFFITS statistic, and the Standardized DFFITS statistic.

Normal probability plot. Normal probability plot of residuals.

Generates data source, if N for input less than. Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.

Deployment. Deployment is available if the Statistica installation is licensed for this feature.

Generates C/C++ code. Generates C/C++ code for deployment of predictive model (for a single dependent variable only).

Generates SVB code. Generates Statistica Visual Basic code for deployment of predictive model (for a single dependent variable only).

Generates PMML code. Generates PMML (Predictive Models Markup Language) code for deployment of predictive model (for a single dependent variable only). This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.

Saves C/C++ code. Save C/C++ code for deployment of predictive model (for a single dependent variable only).

File name for C/C code. Specify the name and location of the file where to save the (C/C++) deployment code information.

Saves SVB code. Save Statistica Visual Basic code for deployment of predictive model (for a single dependent variable only).

File name for SVB code. Specify the name and location of the file where to save the (SVB/VB) deployment code information.

Saves PMML code. Saves PMML (Predictive Models Markup Language) code for deployment of predictive model (for a single dependent variable only). This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.

File name for PMML (XML) code. Specify the name and location of the file where to save the (PMML/XML) deployment code information.